Meaningful Scales Detection: an Unsupervised Noise Detection Algorithm for Digital Contours

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Communicated by Bertrand Kerautret
Demo edited by Bertrand Kerautret

Abstract

This work presents an algorithm which permits to detect locally on digital contour what is the amount of noise estimated from a given maximal scale. The method is based on the asymptotic properties of the length of the maximal segment primitive.

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• source code: TGZ SWHID info
  Software Heritage Archive @softwareversion{sw-ipol.2014.75, title = {{Meaningful Scales Detection: an Unsupervised Noise Detection Algorithm for Digital Contours}}, author = {Bertrand Kerautret, Jacques-Olivier Lachaud}, date = {2013-02-13}, license = {GPL-3.0-or-later}, version = {1.1}, swhid = {swh:1:dir:951986bc75b94af4475e63360f74076b88144cd9;origin=https://doi.org/10.5201/ipol.2014.75;visit=swh:1:snp:63fa8a9b260bf004344c7f9136c4e7041f3ecb5a;anchor=swh:1:rel:d567032916d7b64bf5b2867d78d465f4c4c6908a}, related = {ipol.2014.75}, relatedtype = "Reference article: https://doi.org/10.5201/ipol.2014.75" } % the biblatex-software style extension is recommended for software bibitems
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History

• Note from the editor: the source code was updated on November 28, 2019 to fix a problem when creating images in meaningfulscaleDemo/src/helper/DrawingXFIG.cxx. The original version is available here.
• Note from the editor: the source code was updated on February 16, 2021 to fix an image conversion problem due to a new version of the 'convert' command. The previous version of the code is available here.
• Note from the editor: the manuscript of the article was modified on 2022-01-01 to include information about its editors. The original version of the manuscript is available here.